Publication
FD-ZKF: A zonotopic Kalman filter optimizing fault detection rather than state estimation
Journal Article (2019)
Journal
Journal of Process Control
Pages
89-102
Volume
73
Doc link
https://doi.org/10.1016/j.jprocont.2018.12.003
File
Authors
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Pourasghar Lafmejani, Masoud
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Combastel, Christophe
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Puig Cayuela, Vicenç
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Ocampo Martínez, Carlos A.
Projects associated
Abstract
Enhancing the sensitivity to faults with respect to disturbances, rather than optimizing the precision of the state estimation using Kalman Filters (KF) is the subject of this paper. The stochastic paradigm (KF) is based on minimizing the trace of the state estimation error covariance. In the context of the bounded-error paradigm using Zonotopic Kalman Filters (ZKF), this is analog to minimize the covariation trace. From this analogy and keeping a similar observer-based structure as in ZKF, a criterion jointly inspired by set-membership approaches and approximate decoupling techniques coming from parity-space residual generation is proposed. Its on-line maximization provides an optimal time-varying observer gain leading to the so-called FD-ZKF filter that allows enhancing the fault detection properties. The characterization of minimum detectable fault magnitude is done based on a sensitivity analysis. The effect of the uncertainty is addressed using a set-membership approach and a zonotopic representation reducing set operations to simple matrix calculations. A case study based on a quadruple-tank system is used both to illustrate and compare the effectiveness of the results obtained from the FD-ZKF approach compared to a pure ZKF approach.
Categories
control theory, optimisation.
Author keywords
Uncertain systems, Observers, Fault detection, Bounded uncertainties, Zonotopes, Sensitivity analysis, Minimum detectable fault
Scientific reference
M. Pourasghar, C. Combastel, V. Puig and C. Ocampo-Martínez. FD-ZKF: A zonotopic Kalman filter optimizing fault detection rather than state estimation. Journal of Process Control, 73: 89-102, 2019.
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